CDMA systems operating with high data rates loose some of their inherent protection against intra-cell and inter-cell interference as the spreading gain is reduced when the spreading factor is lowered. Direct Sequence CDMA (DS-CDMA) systems such as those compatible with Rel6 and Rel7 of the Wideband CDMA (WCDMA) standard and EV-DO (Evolution-Data Optimized) based systems are designed to use CDMA for robustly combating interference. Intra-cell interference is particularly troublesome in the uplink direction (i.e., the transmission path from a mobile station to a base station) as then uplink transmission is unsynchronized. Inter-cell interference can be reduced by employing a Time-Division Multiplexing Access (TDMA) method in the uplink direction. WCDMA allows for TDMA in a loose sense in that users can be synchronized in the downlink direction (i.e., the transmission path from a base station to a mobile station to) in a TDMA-like manner with a resolution of 256 chips where a chip is the fundamental unit of transmission in CDMA systems. Users in the uplink direction can then be scheduled in a TDMA-like manner by spacing different user uplink transmissions apart in time. Loose TDMA scheduling enables high data rates in the uplink direction while attaining very high spectral efficiency (assuming full buffer traffic).
However, as recognized by Applicants, multiple user transmissions can overlap during a fraction of the same transmission time interval (TTI) under some conditions. The signal overlap occurs at the beginning and/or end of the TTI, causing very high interference spikes. A similar situation occurs when a multi-user detection receiver is used, where interference associated with one or more radio links is cancelled before detecting and decoding the signal of a particular user. The cancellation efficiency depends on how well the interference signals associated with other radio links are regenerated, which in turn depends on the decoding performance of the radio links. During time intervals when the cancellation efficiency is low, the decoding of the user signal of interest may be subject to very high interference spikes.
Receivers account for interference by soft scaling received signals. Soft scaling is a confidence weighting technique that accounts for the level of interference present in a received signal. The higher the interference, the less reliable the signal values of the received signal and thus less weight given to the signal values. The opposite applies when interference is lower because the received signal values are more reliable. Automatic gain control (AGC) is one type of soft scaling technique. AGC involves increasing receiver dynamic range by scaling received signal values to a predefined level. Maximum ratio combining (MRC) is another soft scaling approach where received baseband signals are propagated via multiple channels and antennas with the goal of maximizing the combiner output signal-to-noise (plus interference) ratio. More advanced receivers such as the G-RAKE (Generalized RAKE) weight signals with an impairment correlation matrix which is a function of the received interference power.
With each of these soft scaling approaches, scaling factors or weights are usually determined based on measurements of the received signals within a predefined averaging period. Interference is smoothed over a long time period when a large averaging period is used. However, a large averaging period yields scaling factors that are insensitive to instantaneous or fast changing interference spikes. Moreover, the receiver slowly adapts to interference spikes when a large averaging period is employed, causing performance loss due to incorrect or biased scaling. On the other hand, a small averaging period more accurately captures the influence of fast changing interference spikes. However a small averaging period causes more fluctuation in the signal scaling factors when interference spikes are not present or dominant. Filtering can be used to smooth out interference for recovering the desired signal. However, when the interference is pulsed and very strong, filtering propagates the interferer over the useful signal samples even though the signal samples were not polluted by the interferer prior to filtering. Accordingly, estimating fast changing interference makes accurate soft scaling very challenging.